DEVELOPMENT... OpenML
Run
160793

Run 160793

Task 3500 (Supervised Classification) squash-unstored Uploaded 14-02-2015 by Felicia West
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Flow

weka.LogitBoost_DecisionStump(2)J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
weka.LogitBoost_DecisionStump(2)_E1
weka.LogitBoost_DecisionStump(2)_H1.0
weka.LogitBoost_DecisionStump(2)_I160
weka.LogitBoost_DecisionStump(2)_L-1.7976931348623157E308
weka.LogitBoost_DecisionStump(2)_O1
weka.LogitBoost_DecisionStump(2)_P100
weka.LogitBoost_DecisionStump(2)_S1
weka.LogitBoost_DecisionStump(2)_Wweka.classifiers.trees.DecisionStump
weka.LogitBoost_DecisionStump(2)_Z3.0

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

model
Model readable

A human-readable description of the model that was built.

model
Model serialized

A serialized description of the model that can be read by the tool that generated it.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

22 Evaluation measures

Area under ROC curve
0.8164
Per class
F measure
0.7391
Per class
Kappa
0.542
KB relative information score
27.5749
Mean absolute error
0.1692
Mean prior absolute error
0.3823
Number of instances
52
Per class
OS information
[Sun Microsystems Inc., 1.6.0_20, amd64, Linux, 2.6.35.14-106.fc14.x86_64]
Precision
0.7643
Per class
Predictive accuracy
0.75
Prior entropy
1.3486
Recall
0.75
Per class
Relative absolute error
0.4427
Root mean prior squared error
0.4353
Root mean squared error
0.4068
Root relative squared error
0.9345
SciMark benchmark
945.7621
UserCPU time millis
90
UserCPU time millis training
90